MISSING VALUES HANDLING METHODS IN R FOR MACHINE LEARNING
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Abstract
The pre-processing section of the machine learning as well as data science is one of the crucial sections which denotes to the basic building block. It means the data preparation is most significant part of the implementing any types of machine learning algorithm as well as models. The data pre-processing comprises various stages like missing values handling, outliers’ detection and correction, data encoding, normalization of numeric values, handling of class imbalance etc... The missing values handling is one of the elementary and well as most significant section which is very necessary to deal with carefulness. There is no chance to avoid or ignore the case, as it may affect the final result through imposing ambiguities in result. Thus, there is always need to deal with missing values as per situation and conditions. It is also tackled according to the algorithms and their working nature. Various programming language and software packages are also providing approaches to deal with the same. In this research study we are trying to explore the capability of R programming with reference of various machine learning algorithms. This is explored with the nature of machine learning algorithms
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